ClustGeo: an R package for hierarchical clustering with spatial constraints. In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints. Two dissimilarity matrices D0 and D1 are inputted, along with a mixing parameter α∈[0,1]. The dissimilarities can be non euclidean and the weights of the observations can be non uniform. The first matrix gives the dissimilarities in the ”feature space” and the second matrix gives the dissimilarities in the ”constraint space”. The criterion minimized at each stage is a convex combination of the homogeneity criterion calculated with D0 and the homogeneity criterion calculated with D1. The idea is then to determine a value of α which increases the spatial contiguity without deteriorating too much the quality of the solution based on the variables of interest i.e. those of the feature space. This procedure is illustrated on a real dataset using the R package ClustGeo.
Keywords for this software
References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Christophe Ambroise, Alia Dehman, Pierre Neuvial, Guillem Rigaill, Nathalie Vialaneix: Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics (2019) arXiv
- Virgilio Gómez-Rubio; Paula Moraga; John Molitor; Barry Rowlingson: DClusterm: Model-Based Detection of Disease Clusters (2019) not zbMATH
- Chavent, Marie; Kuentz-Simonet, Vanessa; Labenne, Amaury; Saracco, Jérôme: ClustGeo: an R package for hierarchical clustering with spatial constraints (2018)
- Marie Chavent, Vanessa Kuentz-Simonet, Amaury Labenne, J. Saracco: ClustGeo: an R package for hierarchical clustering with spatial constraints. (2017) arXiv